Optimal Threshold Determination for Interpreting Semantic Similarity and Particularity: Application to the Comparison of Gene Sets and Metabolic Pathways Using GO and ChEBI

Bettembourg, Charles; Diot, Christian; Dameron, Olivier

doi:10.1371/journal.pone.0133579

Cited by 7 publications

(3 citation statements)

References 42 publications

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“…GO‐BP enrichment was further analyzed to exclude redundant and similar GO‐BP terms. To this goal, we computed the semantic similarity between each GO‐BP term resulting in the enrichment using Python package GOATOOLS 42 and filtered for redundant GO‐BPs following the reported similarity threshold process 43 . Results were finally compared across the different type of statins to identify specific and common pathways and mechanisms of action.…”

Section: Methodsmentioning

confidence: 99%

Statin therapy and risk of Alzheimer's and age‐related neurodegenerative diseases

Torrandell‐Haro

Branigan

Vitali

et al. 2020

A&D Transl Res & Clin Interv

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Introduction Establishing efficacy of and molecular pathways for statins has the potential to impact incidence of Alzheimer's and age‐related neurodegenerative diseases (NDD). Methods This retrospective cohort study surveyed US‐based Humana claims, which includes prescription and patient records from private‐payer and Medicare insurance. Claims from 288,515 patients, aged 45 years and older, without prior history of NDD or neurological surgery, were surveyed for a diagnosis of NDD starting 1 year following statin exposure. Patients were required to be enrolled with claims data for at least 6 months prior to first statin prescription and at least 3 years thereafter. Computational system biology analysis was conducted to determine unique target engagement for each statin. Results Of the 288,515 participants included in the study, 144,214 patients (mean [standard deviation (SD)] age, 67.22 [3.8] years) exposed to statin therapies, and 144,301 patients (65.97 [3.2] years) were not treated with statins. The mean (SD) follow‐up time was 5.1 (2.3) years. Exposure to statins was associated with a lower incidence of Alzheimer's disease (1.10% vs 2.37%; relative risk [RR], 0.4643; 95% confidence interval [CI], 0.44–0.49; P < .001), dementia 3.03% vs 5.39%; RR, 0.56; 95% CI, 0.54–0.58; P < .001), multiple sclerosis (0.08% vs 0.15%; RR, 0.52; 95% CI, 0.41–0.66; P < .001), Parkinson's disease (0.48% vs 0.92%; RR, 0.53; 95% CI, 0.48–0.58; P < .001), and amyotrophic lateral sclerosis (0.02% vs 0.05%; RR, 0.46; 95% CI, 0.30–0.69; P < .001). All NDD incidence for all statins, except for fluvastatin (RR, 0.91; 95% CI, 0.65‐1.30; P = 0.71), was reduced with variances in individual risk profiles. Pathway analysis indicated unique and common profiles associated with risk reduction efficacy. Discussion Benefits and risks of statins relative to neurological outcomes should be considered when prescribed for at‐risk NDD populations. Common statin activated pathways indicate overarching systems required for risk reduction whereas unique targets could advance a precision medicine approach to prevent neurodegenerative diseases.

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Section: Methodsmentioning

confidence: 99%

Statin therapy and risk of Alzheimer's and age‐related neurodegenerative diseases

Torrandell‐Haro

Branigan

Vitali

et al. 2020

A&D Transl Res & Clin Interv

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“…The genes deemed to be biologically related to a SMA-selected marker were those with a semantic similarity above 0.4 for any of the GO terms about the gene(s) associated with the SMA-selected marker (whenever available). The semantic similarity was the metric of Wang et al (2007) described above and a threshold of 0.4 was set as recommended by Bettembourg et al (2015). For each SMA-selected marker, preselected pairs were those involving the selected marker and the markers associated with its biologically related genes.…”

Section: Additive × Additive Marker Effects: Screeningmentioning

confidence: 99%

Candidate Variants for Additive and Interactive Effects on Bioenergy Traits in Switchgrass (Panicum virgatum L.) Identified by Genome‐Wide Association Analyses

et al. 2018

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Core Ideas The genetic architecture of plant height and bioenergy quality was investigated. Interactive effects included interactions with ecotype and pairwise synergies. Statistically valid methods for preselecting pairwise synergies were introduced. Biological information corroborated seven of the effects identified. Effects were related to flowering time, lignin synthesis, plant growth or senescence. Switchgrass (Panicum virgatum L.) is a promising herbaceous energy crop, but further gains in biomass yield and quality must be achieved to enable a viable bioenergy industry. Developing DNA markers can contribute to such progress, but depiction of genetic bases should be reliable, involving simple additive marker effects and also interactions with genetic backgrounds (e.g., ecotypes) or synergies with other markers. We analyzed plant height, C content, N content, and mineral concentration in a diverse panel consisting of 512 genotypes of upland and lowland ecotypes. We performed association analyses based on exome capture sequencing and tested 439,170 markers for marginal effects, 83,290 markers for marker × ecotype interactions, and up to 311,445 marker pairs for pairwise interactions. Analyses of pairwise interactions focused on subsets of marker pairs preselected on the basis of marginal marker effects, gene ontology annotation, and pairwise marker associations. Our tests identified 12 significant effects. Homology and gene expression information corroborated seven effects and indicated plausible causal pathways: flowering time and lignin synthesis for plant height; plant growth and senescence for C content and mineral concentration. Four pairwise interactions were detected, including three interactions preselected on the basis of pairwise marker correlations. Furthermore, a marker × ecotype interaction and a pairwise interaction were confirmed in an independent switchgrass panel. Our analyses identified reliable candidate variants for important bioenergy traits. Moreover, they exemplified the importance of interactive effects for depicting genetic bases and illustrated the usefulness of preselecting marker pairs for identifying pairwise marker interactions in association studies.

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“…All the scores are normalised between 0 and 1 and represent the probability of two sets of MeSH terms to be similar. For this reason, a value of 0.5 (50% probability) is often used as minimum threshold to select the statistically significant comparisons 24 .…”

Section: Resultsmentioning

confidence: 99%

SMAC, a computational system to link literature, biomedical and expression data

Pirro

Gadaleta

Galgani

et al. 2019

Sci Rep

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High-throughput technologies have produced a large amount of experimental and biomedical data creating an urgent need for comprehensive and automated mining approaches. To meet this need, we developed SMAC (SMart Automatic Classification method): a tool to extract, prioritise, integrate and analyse biomedical and molecular data according to user-defined terms. The robust ranking step performed on Medical Subject Headings (MeSH) ensures that papers are prioritised based on specific user requirements. SMAC then retrieves any related molecular data from the Gene Expression Omnibus and performs a wide range of bioinformatics analyses to extract biological insights. These features make SMAC a robust tool to explore the literature around any biomedical topic. SMAC can easily be customised/expanded and is distributed as a Docker container ( https://hub.docker.com/r/hfx320/smac ) ready-to-use on Windows, Mac and Linux OS. SMAC’s functionalities have already been adapted and integrated into the Breast Cancer Now Tissue Bank bioinformatics platform and the Pancreatic Expression Database.

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Optimal Threshold Determination for Interpreting Semantic Similarity and Particularity: Application to the Comparison of Gene Sets and Metabolic Pathways Using GO and ChEBI

Cited by 7 publications

References 42 publications

Statin therapy and risk of Alzheimer's and age‐related neurodegenerative diseases

Statin therapy and risk of Alzheimer's and age‐related neurodegenerative diseases

Candidate Variants for Additive and Interactive Effects on Bioenergy Traits in Switchgrass (Panicum virgatum L.) Identified by Genome‐Wide Association Analyses

SMAC, a computational system to link literature, biomedical and expression data

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